Shrink#

Shrink - 9 #

Version

name: Shrink (GitHub)
domain: main
since_version: 9
function: True
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 9.

Summary

Shrink takes one input data (Tensor<numeric>) and produces one Tensor output, having same datatype and shape with input. It has two attributes, lambd and bias. The formula of this operator is: If x < -lambd, y = x + bias; If x > lambd, y = x - bias; Otherwise, y = 0.

Attributes

bias: The bias value added to output. Default is 0.
lambd: The lambd value for the Shrink formulation. Default is 0.5.

Inputs

input (heterogeneous) - T: The input data as Tensor.

Outputs

output (heterogeneous) - T: The output.

Type Constraints

T in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input to only numeric types.

Examples

_hard_shrink

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Shrink",
    inputs=["x"],
    outputs=["y"],
    lambd=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard")

_soft_shrink

import numpy as np
import onnx

node = onnx.helper.make_node(
    "Shrink",
    inputs=["x"],
    outputs=["y"],
    lambd=1.5,
    bias=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft")

Shrink#

Shrink - 9#

Shrink - 9 #